{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "X, y = fetch_openml('mnist_784', version=1, return_X_y=True)\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    test_size=0.33,\n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Principal component analysis is a generic technique for converting high dimensional data to lower dimensions. We won't cover how it works at all in this lecture, but we'll see what it does, roughly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we create a `PCA` object, and tell it how many features we want to generate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_transformer = PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_transformer.fit(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we now call `transform`, we see that all of our images is represented now by 2 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  568.58845935,  -645.36588514],\n",
       "       [ -201.92982734,  -868.47735568],\n",
       "       [  206.90363151,   -82.97398892],\n",
       "       ...,\n",
       "       [ -979.13193352,  -370.82685518],\n",
       "       [ 1214.80886953,  -424.96503598],\n",
       "       [  782.89542874, -1074.75827279]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_pca = pca_transformer.transform(X_train)\n",
    "X_train_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lm = LogisticRegression()\n",
    "lm.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_pca = pca_transformer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.43294243070362476\n",
      "Test Accuracy: 0.4358874458874459\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "lm_predictions_train_pca = lm.predict(X_train_pca)\n",
    "lm_predictions_test_pca = lm.predict(X_test_pca)\n",
    "print(f\"Training Accuracy: {accuracy_score(y_train, lm_predictions_train_pca)}\")\n",
    "print(f\"Test Accuracy: {accuracy_score(y_test, lm_predictions_test_pca)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that despite having only 2 features (instead of 784), we manage to achieve 44% accuracy. Not bad, given how few features we used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another interesting thing we can do is plot our data on a 2D plot, annotated by the class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2002c5f4288>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=X_train_pca[:, 0],\n",
    "                y=X_train_pca[:, 1],\n",
    "                palette=sns.color_palette(\"hls\", 10),\n",
    "                hue=y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PCA and Clustering on State Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>1789</th>\n",
       "      <th>1792</th>\n",
       "      <th>1796</th>\n",
       "      <th>1800</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>1804</th>\n",
       "      <th>1808</th>\n",
       "      <th>1812</th>\n",
       "      <th>1816</th>\n",
       "      <th>...</th>\n",
       "      <th>1988</th>\n",
       "      <th>1992</th>\n",
       "      <th>1996</th>\n",
       "      <th>2000 ‡</th>\n",
       "      <th>Unnamed: 60</th>\n",
       "      <th>2004</th>\n",
       "      <th>2008</th>\n",
       "      <th>2012</th>\n",
       "      <th>2016 ‡</th>\n",
       "      <th>State.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Arkansas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>California</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>Colorado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>GW</td>\n",
       "      <td>GW</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>Connecticut</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Delaware</td>\n",
       "      <td>GW</td>\n",
       "      <td>GW</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>Delaware</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>D.C.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D.C.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Florida</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>GW</td>\n",
       "      <td>GW</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Georgia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>Hawaii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Idaho</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>Illinois</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Indiana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>Iowa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Kansas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GW</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Kentucky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Louisiana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Maine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>Maine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>GW</td>\n",
       "      <td>GW</td>\n",
       "      <td>F</td>\n",
       "      <td>SP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>Maryland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>GW</td>\n",
       "      <td>GW</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>Massachusetts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>Michigan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>Minnesota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Mississippi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>State</td>\n",
       "      <td>1789</td>\n",
       "      <td>1792</td>\n",
       "      <td>1796</td>\n",
       "      <td>1800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1804</td>\n",
       "      <td>1808</td>\n",
       "      <td>1812</td>\n",
       "      <td>1816</td>\n",
       "      <td>...</td>\n",
       "      <td>1988</td>\n",
       "      <td>1992</td>\n",
       "      <td>1996</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004</td>\n",
       "      <td>2008</td>\n",
       "      <td>2012</td>\n",
       "      <td>2016</td>\n",
       "      <td>State</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Missouri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>Montana</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Montana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Nebraska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>Nevada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>GW</td>\n",
       "      <td>GW</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>DR</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>New Hampshire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>GW</td>\n",
       "      <td>GW</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>F</td>\n",
       "      <td>DR</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>New Jersey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>New Mexico</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>New York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GW</td>\n",
       "      <td>F</td>\n",
       "      <td>DR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>F</td>\n",
       "      <td>DR</td>\n",
       "      <td>...</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GW</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>North Carolina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>North Dakota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>Ohio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Oklahoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>Oregon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>GW</td>\n",
       "      <td>GW</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>Pennsylvania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GW</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>DR</td>\n",
       "      <td>...</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>Rhode Island</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>GW</td>\n",
       "      <td>GW</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>South Carolina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>South Dakota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Tennessee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>Texas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>Utah</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Utah</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GW</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>Vermont</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>GW</td>\n",
       "      <td>GW</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>DR</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>Virginia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>Washington</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>Washington</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>West Virginia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>R</td>\n",
       "      <td>Wisconsin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>Wyoming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>State</td>\n",
       "      <td>1789</td>\n",
       "      <td>1792</td>\n",
       "      <td>1796</td>\n",
       "      <td>1800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1804</td>\n",
       "      <td>1808</td>\n",
       "      <td>1812</td>\n",
       "      <td>1816</td>\n",
       "      <td>...</td>\n",
       "      <td>1988</td>\n",
       "      <td>1992</td>\n",
       "      <td>1996</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004</td>\n",
       "      <td>2008</td>\n",
       "      <td>2012</td>\n",
       "      <td>2016</td>\n",
       "      <td>State</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             State  1789  1792  1796  1800  Unnamed: 5  1804  1808  1812  \\\n",
       "0          Alabama   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "1           Alaska   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "2          Arizona   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "3         Arkansas   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "4       California   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "5         Colorado   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "6      Connecticut    GW    GW     F     F         NaN     F     F     F   \n",
       "7         Delaware    GW    GW     F     F         NaN     F     F     F   \n",
       "8             D.C.   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "9          Florida   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "10         Georgia    GW    GW    DR    DR         NaN    DR    DR    DR   \n",
       "11          Hawaii   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "12           Idaho   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "13        Illinois   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "14         Indiana   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "15            Iowa   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "16          Kansas   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "17        Kentucky   NaN    GW    DR    DR         NaN    DR    DR    DR   \n",
       "18       Louisiana   NaN   NaN   NaN   NaN         NaN   NaN   NaN    DR   \n",
       "19           Maine   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "20        Maryland    GW    GW     F    SP         NaN    DR    DR    DR   \n",
       "21   Massachusetts    GW    GW     F     F         NaN    DR     F     F   \n",
       "22        Michigan   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "23       Minnesota   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "24     Mississippi   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "25           State  1789  1792  1796  1800         NaN  1804  1808  1812   \n",
       "26        Missouri   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "27         Montana   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "28        Nebraska   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "29          Nevada   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "30   New Hampshire    GW    GW     F     F         NaN    DR     F     F   \n",
       "31      New Jersey    GW    GW     F     F         NaN    DR    DR     F   \n",
       "32      New Mexico   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "33        New York   NaN    GW     F    DR         NaN    DR    DR     F   \n",
       "34  North Carolina   NaN    GW    DR    DR         NaN    DR    DR    DR   \n",
       "35    North Dakota   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "36            Ohio   NaN   NaN   NaN   NaN         NaN    DR    DR    DR   \n",
       "37        Oklahoma   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "38          Oregon   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "39    Pennsylvania    GW    GW    DR    DR         NaN    DR    DR    DR   \n",
       "40    Rhode Island   NaN    GW     F     F         NaN    DR     F     F   \n",
       "41  South Carolina    GW    GW    DR    DR         NaN    DR    DR    DR   \n",
       "42    South Dakota   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "43       Tennessee   NaN   NaN    DR    DR         NaN    DR    DR    DR   \n",
       "44           Texas   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "45            Utah   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "46         Vermont   NaN    GW     F     F         NaN    DR    DR    DR   \n",
       "47        Virginia    GW    GW    DR    DR         NaN    DR    DR    DR   \n",
       "48      Washington   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "49   West Virginia   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "50       Wisconsin   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "51         Wyoming   NaN   NaN   NaN   NaN         NaN   NaN   NaN   NaN   \n",
       "52           State  1789  1792  1796  1800         NaN  1804  1808  1812   \n",
       "\n",
       "    1816  ...  1988  1992  1996 2000 ‡ Unnamed: 60  2004  2008  2012 2016 ‡  \\\n",
       "0    NaN  ...     R     R     R      R         NaN     R     R     R      R   \n",
       "1    NaN  ...     R     R     R      R         NaN     R     R     R      R   \n",
       "2    NaN  ...     R     R     D      R         NaN     R     R     R      R   \n",
       "3    NaN  ...     R     D     D      R         NaN     R     R     R      R   \n",
       "4    NaN  ...     R     D     D      D         NaN     D     D     D      D   \n",
       "5    NaN  ...     R     D     R      R         NaN     R     D     D      D   \n",
       "6      F  ...     R     D     D      D         NaN     D     D     D      D   \n",
       "7      F  ...     R     D     D      D         NaN     D     D     D      D   \n",
       "8    NaN  ...     D     D     D      D         NaN     D     D     D      D   \n",
       "9    NaN  ...     R     R     D      R         NaN     R     D     D      R   \n",
       "10    DR  ...     R     D     R      R         NaN     R     R     R      R   \n",
       "11   NaN  ...     D     D     D      D         NaN     D     D     D      D   \n",
       "12   NaN  ...     R     R     R      R         NaN     R     R     R      R   \n",
       "13   NaN  ...     R     D     D      D         NaN     D     D     D      D   \n",
       "14    DR  ...     R     R     R      R         NaN     R     D     R      R   \n",
       "15   NaN  ...     D     D     D      D         NaN     R     D     D      R   \n",
       "16   NaN  ...     R     R     R      R         NaN     R     R     R      R   \n",
       "17    DR  ...     R     D     D      R         NaN     R     R     R      R   \n",
       "18    DR  ...     R     D     D      R         NaN     R     R     R      R   \n",
       "19   NaN  ...     R     D     D      D         NaN     D     D     D      D   \n",
       "20    DR  ...     R     D     D      D         NaN     D     D     D      D   \n",
       "21     F  ...     D     D     D      D         NaN     D     D     D      D   \n",
       "22   NaN  ...     R     D     D      D         NaN     D     D     D      R   \n",
       "23   NaN  ...     D     D     D      D         NaN     D     D     D      D   \n",
       "24   NaN  ...     R     R     R      R         NaN     R     R     R      R   \n",
       "25  1816  ...  1988  1992  1996   2000         NaN  2004  2008  2012   2016   \n",
       "26   NaN  ...     R     D     D      R         NaN     R     R     R      R   \n",
       "27   NaN  ...     R     D     R      R         NaN     R     R     R      R   \n",
       "28   NaN  ...     R     R     R      R         NaN     R     R     R      R   \n",
       "29   NaN  ...     R     D     D      R         NaN     R     D     D      D   \n",
       "30    DR  ...     R     D     D      R         NaN     D     D     D      D   \n",
       "31    DR  ...     R     D     D      D         NaN     D     D     D      D   \n",
       "32   NaN  ...     R     D     D      D         NaN     R     D     D      D   \n",
       "33    DR  ...     D     D     D      D         NaN     D     D     D      D   \n",
       "34    DR  ...     R     R     R      R         NaN     R     D     R      R   \n",
       "35   NaN  ...     R     R     R      R         NaN     R     R     R      R   \n",
       "36    DR  ...     R     D     D      R         NaN     R     D     D      R   \n",
       "37   NaN  ...     R     R     R      R         NaN     R     R     R      R   \n",
       "38   NaN  ...     D     D     D      D         NaN     D     D     D      D   \n",
       "39    DR  ...     R     D     D      D         NaN     D     D     D      R   \n",
       "40    DR  ...     D     D     D      D         NaN     D     D     D      D   \n",
       "41    DR  ...     R     R     R      R         NaN     R     R     R      R   \n",
       "42   NaN  ...     R     R     R      R         NaN     R     R     R      R   \n",
       "43    DR  ...     R     D     D      R         NaN     R     R     R      R   \n",
       "44   NaN  ...     R     R     R      R         NaN     R     R     R      R   \n",
       "45   NaN  ...     R     R     R      R         NaN     R     R     R      R   \n",
       "46    DR  ...     R     D     D      D         NaN     D     D     D      D   \n",
       "47    DR  ...     R     R     R      R         NaN     R     D     D      D   \n",
       "48   NaN  ...     D     D     D      D         NaN     D     D     D      D   \n",
       "49   NaN  ...     D     D     D      R         NaN     R     R     R      R   \n",
       "50   NaN  ...     D     D     D      D         NaN     D     D     D      R   \n",
       "51   NaN  ...     R     R     R      R         NaN     R     R     R      R   \n",
       "52  1816  ...  1988  1992  1996   2000         NaN  2004  2008  2012   2016   \n",
       "\n",
       "           State.1  \n",
       "0          Alabama  \n",
       "1           Alaska  \n",
       "2          Arizona  \n",
       "3         Arkansas  \n",
       "4       California  \n",
       "5         Colorado  \n",
       "6      Connecticut  \n",
       "7         Delaware  \n",
       "8             D.C.  \n",
       "9          Florida  \n",
       "10         Georgia  \n",
       "11          Hawaii  \n",
       "12           Idaho  \n",
       "13        Illinois  \n",
       "14         Indiana  \n",
       "15            Iowa  \n",
       "16          Kansas  \n",
       "17        Kentucky  \n",
       "18       Louisiana  \n",
       "19           Maine  \n",
       "20        Maryland  \n",
       "21   Massachusetts  \n",
       "22        Michigan  \n",
       "23       Minnesota  \n",
       "24     Mississippi  \n",
       "25           State  \n",
       "26        Missouri  \n",
       "27         Montana  \n",
       "28        Nebraska  \n",
       "29          Nevada  \n",
       "30   New Hampshire  \n",
       "31      New Jersey  \n",
       "32      New Mexico  \n",
       "33        New York  \n",
       "34  North Carolina  \n",
       "35    North Dakota  \n",
       "36            Ohio  \n",
       "37        Oklahoma  \n",
       "38          Oregon  \n",
       "39    Pennsylvania  \n",
       "40    Rhode Island  \n",
       "41  South Carolina  \n",
       "42    South Dakota  \n",
       "43       Tennessee  \n",
       "44           Texas  \n",
       "45            Utah  \n",
       "46         Vermont  \n",
       "47        Virginia  \n",
       "48      Washington  \n",
       "49   West Virginia  \n",
       "50       Wisconsin  \n",
       "51         Wyoming  \n",
       "52           State  \n",
       "\n",
       "[53 rows x 66 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"data_set\\presidential-elections.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1972_to_2016 = (df.iloc[:, -14:].drop(['Unnamed: 60'],\n",
    "                                         axis=1).rename(columns={\n",
    "                                             \"2000 ‡\": \"2000\",\n",
    "                                             \"2016 ‡\": \"2016\",\n",
    "                                             \"State.1\": \"State\"\n",
    "                                         }).drop([25, 52]).set_index(\"State\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1972_to_2016 = df_1972_to_2016.replace(\"R\", 1)\n",
    "df_1972_to_2016 = df_1972_to_2016.replace(\"D\", 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1972</th>\n",
       "      <th>1976</th>\n",
       "      <th>1980</th>\n",
       "      <th>1984</th>\n",
       "      <th>1988</th>\n",
       "      <th>1992</th>\n",
       "      <th>1996</th>\n",
       "      <th>2000</th>\n",
       "      <th>2004</th>\n",
       "      <th>2008</th>\n",
       "      <th>2012</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Alaska</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Arizona</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Arkansas</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Colorado</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Connecticut</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Delaware</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>D.C.</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Florida</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Georgia</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hawaii</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Idaho</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Illinois</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Indiana</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Iowa</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Kansas</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Kentucky</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Louisiana</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Maine</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Maryland</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Michigan</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Minnesota</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mississippi</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Missouri</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Montana</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Nebraska</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Nevada</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New Jersey</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New Mexico</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New York</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>North Carolina</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>North Dakota</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Ohio</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Oregon</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>South Carolina</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>South Dakota</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Tennessee</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Texas</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Vermont</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Virginia</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Washington</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>West Virginia</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Wyoming</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                1972  1976  1980  1984  1988  1992  1996  2000  2004  2008  \\\n",
       "State                                                                        \n",
       "Alabama            1     0     1     1     1     1     1     1     1     1   \n",
       "Alaska             1     1     1     1     1     1     1     1     1     1   \n",
       "Arizona            1     1     1     1     1     1     0     1     1     1   \n",
       "Arkansas           1     0     1     1     1     0     0     1     1     1   \n",
       "California         1     1     1     1     1     0     0     0     0     0   \n",
       "Colorado           1     1     1     1     1     0     1     1     1     0   \n",
       "Connecticut        1     1     1     1     1     0     0     0     0     0   \n",
       "Delaware           1     0     1     1     1     0     0     0     0     0   \n",
       "D.C.               0     0     0     0     0     0     0     0     0     0   \n",
       "Florida            1     0     1     1     1     1     0     1     1     0   \n",
       "Georgia            1     0     0     1     1     0     1     1     1     1   \n",
       "Hawaii             1     0     0     1     0     0     0     0     0     0   \n",
       "Idaho              1     1     1     1     1     1     1     1     1     1   \n",
       "Illinois           1     1     1     1     1     0     0     0     0     0   \n",
       "Indiana            1     1     1     1     1     1     1     1     1     0   \n",
       "Iowa               1     1     1     1     0     0     0     0     1     0   \n",
       "Kansas             1     1     1     1     1     1     1     1     1     1   \n",
       "Kentucky           1     0     1     1     1     0     0     1     1     1   \n",
       "Louisiana          1     0     1     1     1     0     0     1     1     1   \n",
       "Maine              1     1     1     1     1     0     0     0     0     0   \n",
       "Maryland           1     0     0     1     1     0     0     0     0     0   \n",
       "Massachusetts      0     0     1     1     0     0     0     0     0     0   \n",
       "Michigan           1     1     1     1     1     0     0     0     0     0   \n",
       "Minnesota          1     0     0     0     0     0     0     0     0     0   \n",
       "Mississippi        1     0     1     1     1     1     1     1     1     1   \n",
       "Missouri           1     0     1     1     1     0     0     1     1     1   \n",
       "Montana            1     1     1     1     1     0     1     1     1     1   \n",
       "Nebraska           1     1     1     1     1     1     1     1     1     1   \n",
       "Nevada             1     1     1     1     1     0     0     1     1     0   \n",
       "New Hampshire      1     1     1     1     1     0     0     1     0     0   \n",
       "New Jersey         1     1     1     1     1     0     0     0     0     0   \n",
       "New Mexico         1     1     1     1     1     0     0     0     1     0   \n",
       "New York           1     0     1     1     0     0     0     0     0     0   \n",
       "North Carolina     1     0     1     1     1     1     1     1     1     0   \n",
       "North Dakota       1     1     1     1     1     1     1     1     1     1   \n",
       "Ohio               1     0     1     1     1     0     0     1     1     0   \n",
       "Oklahoma           1     1     1     1     1     1     1     1     1     1   \n",
       "Oregon             1     1     1     1     0     0     0     0     0     0   \n",
       "Pennsylvania       1     0     1     1     1     0     0     0     0     0   \n",
       "Rhode Island       1     0     0     1     0     0     0     0     0     0   \n",
       "South Carolina     1     0     1     1     1     1     1     1     1     1   \n",
       "South Dakota       1     1     1     1     1     1     1     1     1     1   \n",
       "Tennessee          1     0     1     1     1     0     0     1     1     1   \n",
       "Texas              1     0     1     1     1     1     1     1     1     1   \n",
       "Utah               1     1     1     1     1     1     1     1     1     1   \n",
       "Vermont            1     1     1     1     1     0     0     0     0     0   \n",
       "Virginia           1     1     1     1     1     1     1     1     1     0   \n",
       "Washington         1     1     1     1     0     0     0     0     0     0   \n",
       "West Virginia      1     0     0     1     0     0     0     1     1     1   \n",
       "Wisconsin          1     0     1     1     0     0     0     0     0     0   \n",
       "Wyoming            1     1     1     1     1     1     1     1     1     1   \n",
       "\n",
       "                2012  2016  \n",
       "State                       \n",
       "Alabama            1     1  \n",
       "Alaska             1     1  \n",
       "Arizona            1     1  \n",
       "Arkansas           1     1  \n",
       "California         0     0  \n",
       "Colorado           0     0  \n",
       "Connecticut        0     0  \n",
       "Delaware           0     0  \n",
       "D.C.               0     0  \n",
       "Florida            0     1  \n",
       "Georgia            1     1  \n",
       "Hawaii             0     0  \n",
       "Idaho              1     1  \n",
       "Illinois           0     0  \n",
       "Indiana            1     1  \n",
       "Iowa               0     1  \n",
       "Kansas             1     1  \n",
       "Kentucky           1     1  \n",
       "Louisiana          1     1  \n",
       "Maine              0     0  \n",
       "Maryland           0     0  \n",
       "Massachusetts      0     0  \n",
       "Michigan           0     1  \n",
       "Minnesota          0     0  \n",
       "Mississippi        1     1  \n",
       "Missouri           1     1  \n",
       "Montana            1     1  \n",
       "Nebraska           1     1  \n",
       "Nevada             0     0  \n",
       "New Hampshire      0     0  \n",
       "New Jersey         0     0  \n",
       "New Mexico         0     0  \n",
       "New York           0     0  \n",
       "North Carolina     1     1  \n",
       "North Dakota       1     1  \n",
       "Ohio               0     1  \n",
       "Oklahoma           1     1  \n",
       "Oregon             0     0  \n",
       "Pennsylvania       0     1  \n",
       "Rhode Island       0     0  \n",
       "South Carolina     1     1  \n",
       "South Dakota       1     1  \n",
       "Tennessee          1     1  \n",
       "Texas              1     1  \n",
       "Utah               1     1  \n",
       "Vermont            0     0  \n",
       "Virginia           0     0  \n",
       "Washington         0     0  \n",
       "West Virginia      1     1  \n",
       "Wisconsin          0     1  \n",
       "Wyoming            1     1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1972_to_2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1972</th>\n",
       "      <th>1976</th>\n",
       "      <th>1980</th>\n",
       "      <th>1984</th>\n",
       "      <th>1988</th>\n",
       "      <th>1992</th>\n",
       "      <th>1996</th>\n",
       "      <th>2000</th>\n",
       "      <th>2004</th>\n",
       "      <th>2008</th>\n",
       "      <th>2012</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Michigan</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Minnesota</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mississippi</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Missouri</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Montana</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Nebraska</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Nevada</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               1972  1976  1980  1984  1988  1992  1996  2000  2004  2008  \\\n",
       "State                                                                       \n",
       "Massachusetts     0     0     1     1     0     0     0     0     0     0   \n",
       "Michigan          1     1     1     1     1     0     0     0     0     0   \n",
       "Minnesota         1     0     0     0     0     0     0     0     0     0   \n",
       "Mississippi       1     0     1     1     1     1     1     1     1     1   \n",
       "Missouri          1     0     1     1     1     0     0     1     1     1   \n",
       "Montana           1     1     1     1     1     0     1     1     1     1   \n",
       "Nebraska          1     1     1     1     1     1     1     1     1     1   \n",
       "Nevada            1     1     1     1     1     0     0     1     1     0   \n",
       "\n",
       "               2012  2016  \n",
       "State                      \n",
       "Massachusetts     0     0  \n",
       "Michigan          0     1  \n",
       "Minnesota         0     0  \n",
       "Mississippi       1     1  \n",
       "Missouri          1     1  \n",
       "Montana           1     1  \n",
       "Nebraska          1     1  \n",
       "Nevada            0     0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1972_to_2016.loc[\"Massachusetts\":\"Nevada\", :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "state_data_dimensionality_reducer = Pipeline([('scale', StandardScaler()),\n",
    "                                              ('pca', PCA(n_components=2))])\n",
    "\n",
    "state_data_dimensionality_reducer.fit(df_1972_to_2016)\n",
    "state_vote_data_2d = pd.DataFrame(\n",
    "    state_data_dimensionality_reducer.transform(df_1972_to_2016),\n",
    "    columns=[\"pc1\", \"pc2\"])\n",
    "state_vote_data_2d[\"state\"] = list(df_1972_to_2016.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pc1</th>\n",
       "      <th>pc2</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-2.733898</td>\n",
       "      <td>0.878935</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-2.854135</td>\n",
       "      <td>-0.068869</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-2.187073</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-1.399352</td>\n",
       "      <td>0.733236</td>\n",
       "      <td>Arkansas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2.117444</td>\n",
       "      <td>-1.848357</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>-0.059500</td>\n",
       "      <td>-1.355139</td>\n",
       "      <td>Colorado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2.117444</td>\n",
       "      <td>-1.848357</td>\n",
       "      <td>Connecticut</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2.237681</td>\n",
       "      <td>-0.900553</td>\n",
       "      <td>Delaware</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>4.981848</td>\n",
       "      <td>5.251487</td>\n",
       "      <td>D.C.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>-0.622666</td>\n",
       "      <td>-0.104215</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>-1.580177</td>\n",
       "      <td>2.199467</td>\n",
       "      <td>Georgia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>3.356628</td>\n",
       "      <td>1.214109</td>\n",
       "      <td>Hawaii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>-2.854135</td>\n",
       "      <td>-0.068869</td>\n",
       "      <td>Idaho</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2.117444</td>\n",
       "      <td>-1.848357</td>\n",
       "      <td>Illinois</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>-2.155569</td>\n",
       "      <td>-0.519262</td>\n",
       "      <td>Indiana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.315652</td>\n",
       "      <td>-0.574206</td>\n",
       "      <td>Iowa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>-2.854135</td>\n",
       "      <td>-0.068869</td>\n",
       "      <td>Kansas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>-1.399352</td>\n",
       "      <td>0.733236</td>\n",
       "      <td>Kentucky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>-1.399352</td>\n",
       "      <td>0.733236</td>\n",
       "      <td>Louisiana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>2.117444</td>\n",
       "      <td>-1.848357</td>\n",
       "      <td>Maine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>2.723918</td>\n",
       "      <td>0.472811</td>\n",
       "      <td>Maryland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>3.658597</td>\n",
       "      <td>1.619562</td>\n",
       "      <td>Massachusetts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.434463</td>\n",
       "      <td>-1.505201</td>\n",
       "      <td>Michigan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>4.193642</td>\n",
       "      <td>3.472669</td>\n",
       "      <td>Minnesota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>-2.733898</td>\n",
       "      <td>0.878935</td>\n",
       "      <td>Mississippi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>-1.399352</td>\n",
       "      <td>0.733236</td>\n",
       "      <td>Missouri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>-2.186651</td>\n",
       "      <td>-0.121701</td>\n",
       "      <td>Montana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>-2.854135</td>\n",
       "      <td>-0.068869</td>\n",
       "      <td>Nebraska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.607562</td>\n",
       "      <td>-1.448006</td>\n",
       "      <td>Nevada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.359084</td>\n",
       "      <td>-1.637703</td>\n",
       "      <td>New Hampshire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>2.117444</td>\n",
       "      <td>-1.848357</td>\n",
       "      <td>New Jersey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.365922</td>\n",
       "      <td>-1.658660</td>\n",
       "      <td>New Mexico</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>2.870392</td>\n",
       "      <td>-0.159255</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>-2.035331</td>\n",
       "      <td>0.428542</td>\n",
       "      <td>North Carolina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>-2.854135</td>\n",
       "      <td>-0.068869</td>\n",
       "      <td>North Dakota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.044818</td>\n",
       "      <td>-0.157047</td>\n",
       "      <td>Ohio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>-2.854135</td>\n",
       "      <td>-0.068869</td>\n",
       "      <td>Oklahoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>2.750155</td>\n",
       "      <td>-1.107059</td>\n",
       "      <td>Oregon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.554701</td>\n",
       "      <td>-0.557397</td>\n",
       "      <td>Pennsylvania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>3.356628</td>\n",
       "      <td>1.214109</td>\n",
       "      <td>Rhode Island</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>-2.733898</td>\n",
       "      <td>0.878935</td>\n",
       "      <td>South Carolina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>-2.854135</td>\n",
       "      <td>-0.068869</td>\n",
       "      <td>South Dakota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>-1.399352</td>\n",
       "      <td>0.733236</td>\n",
       "      <td>Tennessee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>-2.733898</td>\n",
       "      <td>0.878935</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>-2.854135</td>\n",
       "      <td>-0.068869</td>\n",
       "      <td>Utah</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>2.117444</td>\n",
       "      <td>-1.848357</td>\n",
       "      <td>Vermont</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>-0.726984</td>\n",
       "      <td>-1.302307</td>\n",
       "      <td>Virginia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>2.750155</td>\n",
       "      <td>-1.107059</td>\n",
       "      <td>Washington</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>-0.280405</td>\n",
       "      <td>2.847898</td>\n",
       "      <td>West Virginia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>2.187411</td>\n",
       "      <td>0.183901</td>\n",
       "      <td>Wisconsin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>-2.854135</td>\n",
       "      <td>-0.068869</td>\n",
       "      <td>Wyoming</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         pc1       pc2           state\n",
       "0  -2.733898  0.878935         Alabama\n",
       "1  -2.854135 -0.068869          Alaska\n",
       "2  -2.187073 -0.161736         Arizona\n",
       "3  -1.399352  0.733236        Arkansas\n",
       "4   2.117444 -1.848357      California\n",
       "5  -0.059500 -1.355139        Colorado\n",
       "6   2.117444 -1.848357     Connecticut\n",
       "7   2.237681 -0.900553        Delaware\n",
       "8   4.981848  5.251487            D.C.\n",
       "9  -0.622666 -0.104215         Florida\n",
       "10 -1.580177  2.199467         Georgia\n",
       "11  3.356628  1.214109          Hawaii\n",
       "12 -2.854135 -0.068869           Idaho\n",
       "13  2.117444 -1.848357        Illinois\n",
       "14 -2.155569 -0.519262         Indiana\n",
       "15  1.315652 -0.574206            Iowa\n",
       "16 -2.854135 -0.068869          Kansas\n",
       "17 -1.399352  0.733236        Kentucky\n",
       "18 -1.399352  0.733236       Louisiana\n",
       "19  2.117444 -1.848357           Maine\n",
       "20  2.723918  0.472811        Maryland\n",
       "21  3.658597  1.619562   Massachusetts\n",
       "22  1.434463 -1.505201        Michigan\n",
       "23  4.193642  3.472669       Minnesota\n",
       "24 -2.733898  0.878935     Mississippi\n",
       "25 -1.399352  0.733236        Missouri\n",
       "26 -2.186651 -0.121701         Montana\n",
       "27 -2.854135 -0.068869        Nebraska\n",
       "28  0.607562 -1.448006          Nevada\n",
       "29  1.359084 -1.637703   New Hampshire\n",
       "30  2.117444 -1.848357      New Jersey\n",
       "31  1.365922 -1.658660      New Mexico\n",
       "32  2.870392 -0.159255        New York\n",
       "33 -2.035331  0.428542  North Carolina\n",
       "34 -2.854135 -0.068869    North Dakota\n",
       "35  0.044818 -0.157047            Ohio\n",
       "36 -2.854135 -0.068869        Oklahoma\n",
       "37  2.750155 -1.107059          Oregon\n",
       "38  1.554701 -0.557397    Pennsylvania\n",
       "39  3.356628  1.214109    Rhode Island\n",
       "40 -2.733898  0.878935  South Carolina\n",
       "41 -2.854135 -0.068869    South Dakota\n",
       "42 -1.399352  0.733236       Tennessee\n",
       "43 -2.733898  0.878935           Texas\n",
       "44 -2.854135 -0.068869            Utah\n",
       "45  2.117444 -1.848357         Vermont\n",
       "46 -0.726984 -1.302307        Virginia\n",
       "47  2.750155 -1.107059      Washington\n",
       "48 -0.280405  2.847898   West Virginia\n",
       "49  2.187411  0.183901       Wisconsin\n",
       "50 -2.854135 -0.068869         Wyoming"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_vote_data_2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2000135aa08>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=state_vote_data_2d, x=\"pc1\", y=\"pc2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or we can create a nicer plot with labels with the code below (requires plotly)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_vote_data_2d_df = pd.DataFrame(state_vote_data_2d, columns=[\"pc1\", \"pc2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "pc1=%{x}<br>pc2=%{y}<br>state=%{text}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "#636efa",
          "symbol": "circle"
         },
         "mode": "markers+text",
         "name": "",
         "orientation": "v",
         "showlegend": false,
         "text": [
          "Alabama",
          "Alaska",
          "Arizona",
          "Arkansas",
          "California",
          "Colorado",
          "Connecticut",
          "Delaware",
          "D.C.",
          "Florida",
          "Georgia",
          "Hawaii",
          "Idaho",
          "Illinois",
          "Indiana",
          "Iowa",
          "Kansas",
          "Kentucky",
          "Louisiana",
          "Maine",
          "Maryland",
          "Massachusetts",
          "Michigan",
          "Minnesota",
          "Mississippi",
          "Missouri",
          "Montana",
          "Nebraska",
          "Nevada",
          "New Hampshire",
          "New Jersey",
          "New Mexico",
          "New York",
          "North Carolina",
          "North Dakota",
          "Ohio",
          "Oklahoma",
          "Oregon",
          "Pennsylvania",
          "Rhode Island",
          "South Carolina",
          "South Dakota",
          "Tennessee",
          "Texas",
          "Utah",
          "Vermont",
          "Virginia",
          "Washington",
          "West Virginia",
          "Wisconsin",
          "Wyoming"
         ],
         "textposition": "top center",
         "type": "scatter",
         "x": [
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          2.7374424651303597,
          -0.1259008505718378,
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          -2.922977205451392
         ],
         "xaxis": "x",
         "y": [
          0.8333391308052479,
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          0.5759878297036625,
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         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "autosize": true,
        "legend": {
         "tracegroupgap": 0
        },
        "margin": {
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",
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       "                    Plotly.newPlot(\n",
       "                        '3e8cfa41-43f6-4efe-b878-c916a5e53e97',\n",
       "                        [{\"hovertemplate\": \"pc1=%{x}<br>pc2=%{y}<br>state=%{text}<extra></extra>\", \"legendgroup\": \"\", \"marker\": {\"color\": \"#636efa\", \"symbol\": \"circle\"}, \"mode\": \"markers+text\", \"name\": \"\", \"orientation\": \"v\", \"showlegend\": false, \"text\": [\"Alabama\", \"Alaska\", \"Arizona\", \"Arkansas\", \"California\", \"Colorado\", \"Connecticut\", \"Delaware\", \"D.C.\", \"Florida\", \"Georgia\", \"Hawaii\", \"Idaho\", \"Illinois\", \"Indiana\", \"Iowa\", \"Kansas\", \"Kentucky\", \"Louisiana\", \"Maine\", \"Maryland\", \"Massachusetts\", \"Michigan\", \"Minnesota\", \"Mississippi\", \"Missouri\", \"Montana\", \"Nebraska\", \"Nevada\", \"New Hampshire\", \"New Jersey\", \"New Mexico\", \"New York\", \"North Carolina\", \"North Dakota\", \"Ohio\", \"Oklahoma\", \"Oregon\", \"Pennsylvania\", \"Rhode Island\", \"South Carolina\", \"South Dakota\", \"Tennessee\", \"Texas\", \"Utah\", \"Vermont\", \"Virginia\", \"Washington\", \"West Virginia\", \"Wisconsin\", \"Wyoming\"], \"textposition\": \"top center\", \"type\": \"scatter\", \"x\": [-2.8532184911947676, -2.9785930454745624, -2.1293288938900825, -1.2642126071891124, 2.2000207595654464, -0.0034359292251523524, 2.0984867526460738, 2.305993968679126, 5.044915682670964, -0.6676547941369022, -1.6097521271918378, 3.294727688371367, -2.953298768412449, 2.105758440665731, -2.2768309761609937, 1.394508657658453, -2.708942243807973, -1.3330811499591668, -1.5603812359720857, 2.150497088125598, 2.738229591137973, 3.55820295171837, 1.3864229479337113, 4.076950624334286, -2.749736148103032, -1.3263595409369153, -2.063006897542364, -2.8442961824879562, 0.7250546184329789, 1.3201812615388084, 2.3164792724937087, 1.4880509847770254, 2.8526513972713063, -2.033082059096161, -2.812047291567454, 0.036334157842251975, -3.0653979791217507, 2.776678085782893, 1.6075661692909466, 3.3194456797366887, -2.6837956605961013, -2.888636862682949, -1.3079145625643402, -2.526809909409824, -2.7297369267092035, 2.0486113897119473, -0.8361468465083898, 2.7374424651303597, -0.1259008505718378, 2.2873499745588894, -2.922977205451392], \"xaxis\": \"x\", \"y\": [0.8333391308052479, 0.08571669031171517, -0.15733068797078467, 0.5759878297036625, -1.8747368990038058, -1.2129898583293348, -1.6858836690819834, -0.8742859468979131, 5.172246036184125, -0.08349007078867986, 2.2698433759744967, 1.2631522593376543, -0.07458362710985798, -1.8237049946454282, -0.5841593216895069, -0.35320796574742647, -0.04562057753933313, 0.5838874532589982, 0.7894267047140415, -1.8332318558266045, 0.4253709782673212, 1.6618872727405276, -1.440115105607809, 3.308208807343317, 0.8521709711013168, 0.6534023594662461, -0.09128960289979418, -0.1567804380008619, -1.5458697940623043, -1.695326690275195, -1.865426047059263, -1.6700820291388356, -0.04203847279839974, 0.39003680067177243, -0.14059920651783397, -0.3546117481695402, -0.1132446127869553, -1.0586349736726162, -0.5717086727210696, 1.363633819776891, 1.039344763126783, -0.024126751145273267, 0.6339292583088554, 0.7925006941500866, 0.014020708619687891, -1.8987227456396374, -1.1526484041915426, -1.0964540013968886, 2.980144907401451, 0.08021192494566151, -0.05011442424574955], \"yaxis\": \"y\"}],\n",
       "                        {\"legend\": {\"tracegroupgap\": 0}, \"margin\": {\"t\": 60}, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, 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\"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"pc1\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"pc2\"}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('3e8cfa41-43f6-4efe-b878-c916a5e53e97');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "state_vote_data_2d_df = pd.DataFrame(state_vote_data_2d, columns=[\"pc1\", \"pc2\"])\n",
    "state_vote_data_2d_jittered_df = np.random.normal(\n",
    "    0, 0.1, state_vote_data_2d_df.shape) + state_vote_data_2d_df\n",
    "\n",
    "import plotly.express as px\n",
    "state_names = list(df_1972_to_2016.index)\n",
    "state_vote_data_2d_jittered_df[\"state\"] = state_names\n",
    "fig = px.scatter(state_vote_data_2d_jittered_df, x=\"pc1\", y=\"pc2\", text=\"state\")\n",
    "\n",
    "fig.update_traces(textposition='top center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clustering on Iris Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>138</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width     species\n",
       "138           6.0          3.0           4.8          1.8   virginica\n",
       "89            5.5          2.5           4.0          1.3  versicolor\n",
       "87            6.3          2.3           4.4          1.3  versicolor\n",
       "133           6.3          2.8           5.1          1.5   virginica\n",
       "98            5.1          2.5           3.0          1.1  versicolor\n",
       "146           6.3          2.5           5.0          1.9   virginica\n",
       "123           6.3          2.7           4.9          1.8   virginica\n",
       "43            5.0          3.5           1.6          0.6      setosa\n",
       "45            4.8          3.0           1.4          0.3      setosa\n",
       "33            5.5          4.2           1.4          0.2      setosa"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = pd.read_csv(r\"data_set\\iris.csv\")\n",
    "iris.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "31            5.4          3.4           1.5          0.4\n",
       "36            5.5          3.5           1.3          0.2\n",
       "55            5.7          2.8           4.5          1.3\n",
       "119           6.0          2.2           5.0          1.5\n",
       "118           7.7          2.6           6.9          2.3\n",
       "80            5.5          2.4           3.8          1.1\n",
       "73            6.1          2.8           4.7          1.2\n",
       "8             4.4          2.9           1.4          0.2\n",
       "25            5.0          3.0           1.6          0.2\n",
       "20            5.4          3.4           1.7          0.2"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = iris.drop(\"species\", axis=1)\n",
    "iris.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20002965b88>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mgSuBZ1F5UN0rkwsvnPoMJC1UTZQcVRO1Xqv6DM4CpeDnCvAgsBQ/PJHu1a56+3o9AFHiGRgYiNw/kCbqZWivRpLBw8A88B7gQ+7+42RCEmmvVn8DLZVKFAoFSqUSQ0NDZLNZzKzm2zkQ+m0/aicw1HYWh63Tt2mJpF6ZUfUCTAF/DHwF+ALwLuBFUbdv5aLSUmmVVtezF4vFmnkGjhw5UtMncOjQoZr6/9nZWV9eXg7tKTh27FhNjNXros5dIP2LOH0GNRtU7hW8GcgDjzW6fSsWJQNplVbXs+fz+Zr91avrr67/zwXzEUQd2439A9JZGyWDRqqJ9pnZfcB7ga3AbwA/G/0cRCR9Wl3PXiqVIs8LUF3/vzofQdSx3dg/IOnVyMXEPwKe4e6/6u7/xd2/4u6Pr75oZi9ufXgiyYo7r0C1oaGhyPMCVNf/54L5CKKOrdc/0Kr3Iv0lcjJw9++4+9kNhry7BfGItFWr69mz2WzNPAM7d+6s6RO48847a+r/V3sHwnoKdu3aVRNj9bqocxeIhIncZ7Dpjsz+zt0vbcnONqE+A2klVRNJv9ioz6CVyeCQu1/Wkp1tQslARKRxrWo6E5EqYWcVEO3beSNnJHHOXtTJK1G08r+IEy3cl0jqraysMD8/z8TEBGNjY0xMTLCwsFCzbn5+npWVlU23DRvX6NhWbit9pl7N6epCnXkM0HwG0ufCehSi1vo30t8QpxdC8wLIWsSczyBsHoNzuQTNZyB9KqxHIWqtfyP9DXF6ITQvgEQVZT4DzWMgEiLsmftR5wpo5Hn9cZ7tr3kBJKqG7hmY2XVm9lYze8fqklRgImkX1qMQtda/kf6GOL0QmhdAompkPoObgPOBXwI+DLwK+La7/2Zy4YVTaamkhaqJpJu0pM/AzObc/blrfj4ZmHb3l7Qy2CiUDEREGteqPoPHgp8/NbOnAj8GdsYNTgS649trnLMAkbRr5L/az5rZBVTmNDhEpa/gE0kEJf2lG2rh68W4sLCQ6rhFompoDmR3X179HTgPeHx1XTvpMlFvOXXqFBMTEzUVLwcPHkzNtIf1YvzABz7Addddt25dmuIWWWujy0SNnBl8c/UXd19294fWrhNpVjfUwteLMWyegTTFLRLVpsnAzEbN7HLgSWZ2qZldFizXUKkuEoml1XMKJKFejGHzDKQpbpGoopwZ/CrwJ8DFwHuAPw2WNwNvTy406RfdUAtfL8aweQbSFLdIVI3cM7je3fclHE8kumfQe1RNJJK8VpWWfsPMbgGe6u4vNbNLgBe4+y0tiVL62sDAQOpvutaLMe1xi0TRyFeYjwCfB54a/P194MaNNjCzW81syczurvO6mdmfmdm9ZjZnZm2ZHEdERNZr5MzgQnf/lJn9PoC7l81sozmRAT4KvB/4WJ3XXwo8PVieD3ww+CkSWdg0kYODtf9pRx0HrX/8A+hykqRbI/81njGzn6Py2GrMbAJ4aKMN3P2rwE82GDIFfCx41PZB4AIzyzYQk/S5crnM3Nwck5OT7N69m8nJSebm5iiXy02Ng9ZPJhN1whuRjqo30UH1AlwGfAP4h+Dn94HnRthuDLi7zmufBV645u//C4xvtk9NbiOrFhcXQydvWVxcbGqce+snk4k64Y1I0og5uc2qI8CdwE+BR4BPBwkhDgtZF1reZGZ7gb0AO3bsiHlY6RXFYjG0GaxYLDY1Dlo/mUzUCW9EOqmRy0QfA54F/DfgfVSu8/9lzOOfBLav+fti4IGwge5+s7uPu/v4yMhIzMNKr8hkMqHNYJlMpqlxEK8JLmzb1QlvmtmfSLs0kgye6e6/5e5fCpa9wDNiHv8u4DeCqqIJ4CF3L8Tcp/SRbDbL9PT0usav6elpstlsU+Og9ZPJRJ3wRqSTGmk6+yhwk1du9GJmzwde4+6v32CbjwPXABcCDwLvBIYA3P0mMzMq1UbXUrn89Dp337SbTE1nspaqiUSiadXkNkeBZwKLwaodwFFgBXB3f24LYo1EyUBEpHGt6kC+tkXxiIhIykROBu6e33yUiIh0I120FBERJQMREVEyEBERlAxERAQlAxERQclARERQMhAREZQMREQEJQMREUHJQEREUDIQERGUDEREBCUDERFByUBERFAyEBERlAxERAQlAxERQclARERQMhAREZQMREQEJQMREUHJQEREgMFOB9AvVlZWWFpaYnl5meHhYbZt28bAgHKxiKSDPo3aYGVlhfn5eSYmJhgbG2NiYoL5+XlWVlY6HZqICKBk0BZLS0tMTU2Rz+cByOfzTE1NsbS01OHIREQqlAzaYHl5+VwiWJXP51leXu5QRCIi6ykZtMHw8DC5XG7dulwux/DwcIciEhFZT8mgDbZt28b+/fvPJYRcLsf+/fvZtm1bhyMTEalQNVEbDAwM8JznPIeDBw+qmkhEUknJoE0GBgYYHR3tdBgiIqES/2pqZtea2ffM7F4ze1vI6681s9NmdjhYfivpmNJiZWWFU6dOkc/nOXXqlEpNRaRjEk0GZrYF+HPgpcAlwA1mdknI0E+6+/OC5cNJxpQW6j0QkTRJ+szgCuBedz/u7kXgE8BUwsfsCuo9EJE0SToZPA24f83fJ4N11a43szkzu8PMtoftyMz2mtmMmc2cPn06iVjbSr0HIpImSScDC1nnVX9/Bhhz9+cC/we4LWxH7n6zu4+7+/jIyEiLw2w/9R6ISJoknQxOAmu/6V8MPLB2gLv/2N1Xvw5/CLg84ZhSQb0HIpImSZeWfgd4upntBH4IvBr49bUDzCzr7oXgz1cARxOOKRXUeyAiaZJoMnD3spm9Efg8sAW41d3vMbM/BGbc/S7gP5jZK4Ay8BPgtUnGlCbqPRCRtDD36kv46Tc+Pu4zMzNtPWYj8xGUSiUKhQKlUomhoSGy2Sxbtmyp2R6ItE/NhSAirWBms+4+HvaaPlEiaKQnoFQqMTc3x1VXXcXu3bu56qqrmJ+fZ2FhoWb7sHXV+1Q/goi0hbt33XL55Zd7OxUKBc/lck6lEsoBz+VyXigUasbm8/nQsQcOHIi0rnqfjRxbRGQjVC7Ph36u6swggkZ6AkqlUujYrVu3RlpXvU/1I4hIOygZRNBIT8DQ0FDo2DNnzkRaV71P9SOISDsoGUTQSE9ANptl375968ZOT0+za9eumu3D1lXvU/0IItIOqiaKSNVEItLtNqom0nwGETXSEzA0NMSOHTtq1odtH2Wf6kcQkaTp66WIiOjMIKpyuUyhUKBYLJLJZMhms7h7zeWgoaGhyJd1dPlHRNJCnzwRlMtl5ubmmJycZPfu3UxOTrKwsMDdd9+9rrlsbm6OYrEYqUlMzWQikiZKBhEUCgX27NmzbiKaH/zgB7zyla9ct+7666+nUChEmrRGk9uISJooGURQLBZrGr+2bt0a2gxWLpcjNYmpmUxE0kTJIIJMJlPT+HXmzJnQZrDBwcFITWJqJhORNFEyiCCbzTI9Pb2u8Wvnzp3ceeed69bt27ePbDYbqUlMzWQikiZqOotI1UQi0u3UdNYCg4ODbN++vWZ9WHNZ1CYxNZOJSFr0VTKI+k087CxgZWWFQqFAuVxmcHDw3LoHH3zw3LqLLrqI8847j8cff7xmvZlx6tSpc+tGR0cZGhrSGYSIpEO9Z1uneWlmPoOzZ8/64cOHz80NkMvl/PDhw3727Nl140qlks/Ozq4bt7i46DMzM+vW3XvvvTXrZmZm/LHHHqtZPzs760eOHKkZu3augnrxRI1bRGQzbDCfQcc/2JtZmkkGUSeJWVxcrBl3/PjxmnVrP9zX7u/EiRORJ7c5ceKEJrcRkbbZKBn0zbWGqHX9YT0FZ8+erVk3MDDQUJ9B2EQ25XJ503jUjyAi7dA3ySBqXX9YT8GWLVtq1q2srDTUZxA2kc3g4GDNOvUjiEgn9E0yiFrXH9ZTMDg4WDNhTSaTqVm3b98+LrrootDJbXbu3Fkzdu0HvfoRRKST+qrPQNVEItLPNuoz6KtkICLSz9R0toE437rrbVssFmvOIjKZTMLvRESkeX19rSHOnAL1tl1eXmZ+fp6rr76a3bt3c/XVVzM/P0+xWGzDOxIRaU5fJ4M4cwrU2/bUqVNcf/31ofMciIikVV8ngzg1/PW2rddnUN1TICKSJn2dDOLU8Nfbtl6fQXVPgYhImvR1MohTw19v29HR0dD+g2w2m9wbERGJqe9LS1VNJCL9QqWlG4gzp0C9bcMeaSEikmaJXyYys2vN7Htmdq+ZvS3k9WEz+2Tw+rfMbCzpmEREZL1Ek4GZbQH+HHgpcAlwg5ldUjXsN4G/d/fdwP8A3p1kTCIiUivpM4MrgHvd/bi7F4FPAFNVY6aA24Lf7wBeZGaWcFwiIrJG0sngacD9a/4+GawLHePuZeAh4Oeqd2Rme81sxsxmTp8+nVC4IiL9KelkEPYNv7p8KcoY3P1mdx939/GRkZGWBCciIhVJJ4OTwPY1f18MPFBvjJkNAv8I+EnCcYmIyBpJl5Z+B3i6me0Efgi8Gvj1qjF3Aa8Bvgm8Cviib9L8MDs7+yMzy280ZhMXAj+KsX2a9NJ7gd56P3ov6dVL76eR91K35j3RZODuZTN7I/B5YAtwq7vfY2Z/SGVi5ruAW4C/NLN7qZwRvDrCfmNdJzKzmXqNF92ml94L9Nb70XtJr156P616L4k3nbn754DPVa17x5rfHwf+edJxiIhIfX39bCIREano12Rwc6cDaKFeei/QW+9H7yW9eun9tOS9dOWD6kREpLX69cxARETWUDIQEZH+SgZmdquZLZnZ3Z2OJS4z225mXzKzo2Z2j5m9qdMxNcvMzjOzb5vZd4P38q5OxxSXmW0xs78zs892Opa4zOyEmc2b2WEza81EIh1iZheY2R1mdiz4f+cFnY6pWWb2zODfyerysJnd2PT++umegZldBTwKfMzdn93peOIwsyyQdfdDZvYUYBb4Z+5+pMOhNSx4MOFWd3/UzIaArwNvcveDHQ6taWb2FmAc+Bl3f3mn44nDzE4A4+7e9U1aZnYb8DV3/7CZZYDz3f0fOh1XXMETon8IPN/dm2rI7aszA3f/Kj3yqAt3L7j7oeD3R4Cj1D4EsCt4xaPBn0PB0rXfUszsYuA64MOdjkWeYGY/A1xFpdEVdy/2QiIIvAi4r9lEAH2WDHpVMCHQpcC3OhtJ84LLKoeBJeAL7t617wX4n8BbgZVOB9IiDvxvM5s1s72dDiaGnwdOAx8JLuF92My2djqoFnk18PE4O1Ay6HJm9mRgH3Cjuz/c6Xia5e5n3f15VB5meIWZdeVlPDN7ObDk7rOdjqWFrnT3y6hMUvWG4HJrNxoELgM+6O6XAmeAmtkXu01wuesVwF/H2Y+SQRcLrq/vA2539+lOx9MKwWn7l4FrOxxKs64EXhFcZ/8E8Mtm9ledDSked38g+LkE3Ell0qpudBI4ueas8w4qyaHbvRQ45O4PxtmJkkGXCm663gIcdff3dDqeOMxsxMwuCH5/EvArwLHORtUcd/99d7/Y3ceonLp/0d3/VYfDapqZbQ0KFAguqbwE6MpqPHc/BdxvZs8MVr0I6LqCixA3EPMSEbThQXVpYmYfB64BLjSzk8A73f2WzkbVtCuBfw3MB9faAd4ePBiw22SB24KKiAHgU+7e9SWZPeIi4M5gJtpB4H+5+992NqRY/j1we3Bp5Tjwug7HE4uZnQ+8GPjt2Pvqp9JSEREJp8tEIiKiZCAiIkoGIiKCkoGIiKBkICIiKBmIiAhKBiIAmNlrzeypEcZ91MxetcHrXzaz8RbHdoGZvX7N39f0wqOxJV2UDEQqXgtsmgw65ALg9ZuOEolByUB6kpmNBROY3GZmc8GEJueb2eVm9pXgCZyfN7Ns8E1/nEpn6mEze5KZvcPMvmNmd5vZzcHjPxqN4SVm9k0zO2Rmfx08VHB1sph3BevnzexZwfoRM/tCsP4vzCxvZhcCfwTsCmL742D3T14zScvtzcQnspaSgfSyZwI3u/tzgYeBNwDvA17l7pcDtwL/1d3vAGaAf+nuz3P3x4D3u/s/CSZBehLQ0AQ1wYf4fwZ+JXji5wzwljVDfhSs/yDwe8G6d1J5ltFlVB4ItyNY/zYqz6p/nrv/x2DdpX3G7CEAAAGmSURBVMCNwCVUHs18ZSPxiVTrq2cTSd+5392/Efz+V8DbgWcDXwi+SG8BCnW2/SUzeytwPvCPgXuAzzRw7AkqH9TfCI6VAb655vXVp8zOAnuC318IvBLA3f/WzP5+g/1/291PAgTPphqjMkOcSFOUDKSXVT946xHgHnffcN5bMzsP+ACVqR7vN7M/AM5r8NhGZZKeG+q8vhz8PMsT/x82cqlnec3va/ch0hRdJpJetmPNhOc3AAeBkdV1ZjZkZr8QvP4I8JTg99UP/h8F1/nrVg9t4CBwpZntDo51vpk9Y5Ntvg78i2D8S4CfDYlNJBFKBtLLjgKvMbM5Kpd63kflg/3dZvZd4DDwT4OxHwVuCi65LAMfAuaBTwPfafTA7n6aSoXSx4PjHwSetclm7wJeYmaHqExYUgAecfcfU7ncdPeaG8giLaVHWEtPCuaF/mxwA7grmNkwcNbdy8HZyweDqUBFEqfrjCLpsQP4lJkNAEXg33Y4HukjOjMQaYKZ3QnsrFr9n9z9852IRyQuJQMREdENZBERUTIQERGUDEREBCUDEREB/j+bx69aDhOHXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=iris, x=\"petal_length\", y=\"petal_width\", color=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=5, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "km = KMeans(n_clusters=5)\n",
    "km.fit(iris[[\"petal_length\", \"petal_width\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 0, 4, 0, 0, 0, 4, 0, 4, 4, 0, 4, 0, 4, 0,\n",
       "       0, 4, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0,\n",
       "       4, 4, 0, 0, 4, 4, 4, 4, 4, 0, 4, 4, 3, 2, 3, 2, 2, 3, 0, 3, 2, 3,\n",
       "       2, 2, 2, 2, 2, 2, 2, 3, 3, 0, 2, 2, 3, 0, 2, 3, 0, 0, 2, 2, 3, 3,\n",
       "       2, 0, 2, 3, 2, 2, 0, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_nums = km.predict(iris[[\"petal_length\", \"petal_width\"]])\n",
    "cluster_nums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20002d8c388>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=iris,\n",
    "                x=\"petal_length\",\n",
    "                y=\"petal_width\",\n",
    "                hue=cluster_nums,\n",
    "                palette=sns.color_palette(\"hls\", 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "#single linkage means use the MINIMUM distance between points in two clusters\n",
    "ac = AgglomerativeClustering(linkage=\"single\", n_clusters=2)\n",
    "cluster_nums = ac.fit_predict(iris[[\"petal_length\", \"petal_width\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20002d86408>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=iris, x=\"petal_length\", y=\"petal_width\", hue=cluster_nums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
